Model-Based Traffic Prediction Using Sensor Networks

Measuring traffic flow plays an important role in intelligent transportation systems. In recent years, the technology of sensor network has been brought into the field due to their reliability and non-intrusiveness. In this paper, we propose a framework to reduce the installation and maintenance cost of traffic measuring sensor networks. The key to the solutions lies on predicting the complete measurements using the readings at a limited number of observing locations. We describe two correlation-based prediction methods and show that the Gaussian method is more informative and achieves better accuracy. We propose an analytical approach that eases the procedure of acquiring the Gaussian parameters. We demonstrate through experimental results that the model is correct and achieves prediction very close to the model learned over a large set of training data.